Collision detection method and apparatus based on an autonomous vehicle, device and storage medium

ABSTRACT

Embodiments of the present application provide a collision detection method and apparatus based on an autonomous vehicle, a device and a storage medium, where the method includes: acquiring first point cloud data of each obstacle in each region around the autonomous vehicle, where the first point cloud data represents coordinate information of the obstacle and the first point cloud data is based on a world coordinate system; converting the first point cloud data of the each obstacle into second point cloud data based on a relative coordinate system, where an origin of the relative coordinate system is a point on the autonomous vehicle; determining, according to the second point cloud data of the each obstacle in all regions, a possibility of collision of the autonomous vehicle. A de-positioning manner for collision detection is provided, thereby improving the reliability and stability of collision detection.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application claims priority to Chinese application No.201811005086.1, filed on Aug. 30, 2018, which is incorporated byreference in its entirety.

TECHNICAL FIELD

The present application relates to the field of autonomous vehicletechnologies, more particularly, to a collision detection method andapparatus based on an autonomous vehicle, a device, and a storagemedium.

BACKGROUND

With the development of the intelligent technology, autonomous vehicleshave been developed and applied. During the driving process of theautonomous vehicles, the autonomous vehicles often meet obstacles, andthen need to avoid the obstacles.

In the prior art, laser radar can be adopted to collect positioninformation of obstacles for an autonomous vehicle, the positioninformation is a kind of point cloud data that is based on ahigh-precision world coordinate system, and the possibility of collisionof the autonomous vehicle can then be calculated according to the pointcloud data.

However, in the prior art, when determining the possibility of collisionof an autonomous vehicle, the world coordinate system is required fordetermining the possibility of collision of the autonomous vehicle, butwhen using the world coordinate system, it is necessary to depend on apositioning module, and further depend on more modules indirectly; andthe subsystem which is used to calculate parameters based on the worldcoordinate system will bring some unreliability, thereby resulting inunreliable collision detection.

SUMMARY

The embodiments of the present application provides a collisiondetection method and apparatus based on an autonomous vehicle, a device,and a storage medium, so as to solve the problem in the above solution.

A first aspect of the present application provides a collision detectionmethod based on an autonomous vehicle, including:

acquiring first point cloud data of each obstacle in each region aroundthe autonomous vehicle, where the first point cloud data representscoordinate information of the obstacle, and the first point cloud datais based on a world coordinate system;

converting the first point cloud data of the each obstacle into secondpoint cloud data based on a relative coordinate system, where an originof the relative coordinate system is a point on the autonomous vehicle;

determining, according to the second point cloud data of the eachobstacle in all regions, a collision risk value, where the collisionrisk value represents a possibility of collision of the autonomousvehicle.

Further, the origin of the relative coordinate system is a center pointof the autonomous vehicle, an X-axis of the relative coordinate systemis a central axis of the autonomous vehicle, a Y-axis of the relativecoordinate system passes through the origin and is perpendicular to theX-axis, the Z-axis of the relative coordinate system passes through theorigin, and the Z-axis of the relative coordinate system isperpendicular to both the X-axis and the Y-axis.

Further, the determining, according to the second point cloud data ofthe each obstacle in all regions, the collision risk value, includes:

determining, according to the second point cloud data of the eachobstacle, an obstacle speed of the each obstacle;

determining, according to obstacle speeds of all obstacles within theeach region, a regional risk value of the each region;

determining, according to the regional risk values of all the regions,the collision risk value.

Further, the determining, according to the second point cloud data ofthe each obstacle, an obstacle speed of the each obstacle, includes:

determining, according to the second point cloud data of the eachobstacle on at least two frames, a displacement value of the eachobstacle;

determining, according to both the displacement value of the eachobstacle and times corresponding to the at least two frames, theobstacle speed of the each obstacle.

Further, after determining, according to the second point cloud data ofthe each obstacle, the obstacle speed of the each obstacle, the methodfurther includes:

acquiring the obstacle speed of the each obstacle on previous N frames,where N is a positive integer great than or equal to 1;

correcting, according to the obstacle speed of the each obstacle on theprevious N frames, the obstacle speed of the each obstacle, to obtain acorrected obstacle speed of the each obstacle.

Further, the determining, according to the obstacle speeds of allobstacles in the each region, a regional risk value of the each region,includes:

performing a weighted calculation on the obstacle speeds of allobstacles in the each region to obtain the regional risk value of theeach region.

Further, the determining, according to the obstacle speeds of allobstacles in the each region, a regional risk value of the each region,includes:

determining, according to the obstacle speeds of all obstacles in theeach region, a test obstacle in the each region;

acquiring an actual distance and a safety distance of the test obstaclein the each region, where the actual distance represents the actualdistance between the test obstacle and the autonomous vehicle, and thesafety distance represents the safety distance between the test obstacleand the autonomous vehicle;

determining a difference between the actual distance and the safetydistance of the test obstacle in the each region as the regional riskvalue of the each region.

Further, the acquiring the actual distance of the test obstacle in theeach region, includes:

determining, according to the second point cloud data of the testobstacle in the each region, the actual distance of the test obstacle inthe each region.

Further, the acquiring the safety distance of the test obstacle in theeach region, includes:

acquiring an autonomous vehicle acceleration and an autonomous vehiclespeed of the autonomous vehicle and acquiring an obstacle accelerationof the test obstacle in the each region;

determining, according to the obstacle acceleration of the test obstaclein the each region, the obstacle speed of the test obstacle in the eachregion, and the autonomous vehicle acceleration and the autonomousvehicle speed, the safety distance of the test obstacle in the eachregion.

Further, the determining, according to the regional risk values of allthe regions, the collision risk value, includes:

performing, according to a preset collision risk weight corresponding tothe each region in a one-to-one relationship, a weighted calculation onthe regional risk values of all the regions, to obtain the collisionrisk value.

Further, the determining, according to the regional risk values of allthe regions, the collision risk value, includes:

perform a calculation on the regional risk values of all the regions byadopting a linear judgment manner to obtain the collision risk value.

A second aspect of the present application provides a collisiondetection apparatus based on an autonomous vehicle, including:

an acquisition unit, configured to acquire first point cloud data ofeach obstacle in each region around the autonomous vehicle, where thefirst point cloud data represents coordinate information of theobstacle, and the first point cloud data is based on a world coordinatesystem;

a conversion unit, configured to convert the first point cloud data ofthe each obstacle into second point cloud data based on a relativecoordinate system, where an origin of the relative coordinate system isa point on the autonomous vehicle;

a determination unit, configured to determine, according to the secondpoint cloud data of the each obstacle in all regions, a collision riskvalue, where the collision risk value represents a possibility ofcollision of the autonomous vehicle.

Further, the origin of the relative coordinate system is a center pointof the autonomous vehicle, an X-axis of the relative coordinate systemis an central axis of the autonomous vehicle, a Y-axis of the relativecoordinate system passes through the origin and is perpendicular to theX-axis, the Z-axis of the relative coordinate system passes through theorigin, and the Z-axis of the relative coordinate system isperpendicular to both the X-axis and the Y-axis.

Further, the determination unit includes:

a first determination module, configured to determine, according to thesecond point cloud data of the each obstacle, an obstacle speed of theeach obstacle;

a second determination module, configured to determine, according to theobstacle speeds of all obstacles in the each region, a regional riskvalue of the each region;

a third determination module, configured to determine, according to theregional risk values of all the regions, the collision risk value.

Further, the first determination module includes:

a first determination sub-module, configured to determine, according tothe second point cloud data of the each obstacle on at least two frames,a displacement value of the each obstacle;

a second determination sub-module, configured to determine, according toboth the displacement value of the each obstacle and times correspondingto the at least two frames, the obstacle speed of the each obstacle.

Further, the determination unit further includes:

an acquisition module, configured to acquire the obstacle speed of theeach obstacle on previous N frames after the first determination moduledetermines, according to the second point cloud data of the eachobstacle, the obstacle speed of the each obstacle, where N is a positiveinteger great than or equal to 1;

a correction module, configured to correct, according to the obstaclespeed of the each obstacle on the previous N frames, the obstacle speedof the each obstacle, to obtain a corrected obstacle speed of the eachobstacle.

Further, the second determination module includes:

a calculation sub-module, configured to perform a weighted calculationon the obstacle speeds of all obstacles in the each region to obtain theregional risk value of the each region.

Further, the second determination module includes:

a third determination sub-module, configured to determine, according tothe obstacle speeds of all obstacles in the each region, a test obstaclein the each region;

an acquisition sub-module, configured to acquire an actual distance anda safety distance of the test obstacle in the each region, where theactual distance represents the actual distance between the test obstacleand the autonomous vehicle, and the safety distance represents thesafety distance between the test obstacle and the autonomous vehicle;

a confirmation sub-module, configured to determine a difference betweenthe actual distance and the safety distance of the test obstacle in theeach region as the regional risk value of the each region.

Further, the acquisition sub-module is specifically configured to:

determine, according to the second point cloud data of the test obstaclein the each region, the actual distance of the test obstacle in the eachregion.

Further, the acquisition sub-module is specifically configured to:

acquire an autonomous vehicle acceleration and an autonomous vehiclespeed of the autonomous vehicle and acquire an obstacle acceleration ofthe test obstacle in the each region;

determine, according to the obstacle acceleration of the test obstaclein the each region, the obstacle speed of the test obstacle in the eachregion, and the autonomous vehicle acceleration and the autonomousvehicle speed, the safety distance of the test obstacle in the eachregion.

Further, the third determination module is specifically configured to:

perform, according to a preset collision risk weight corresponding tothe each region in a one-to-one relationship, a weighted calculation onthe regional risk values of all the regions to obtain the collision riskvalue.

Further, the third determination module is specifically configured to:

perform a calculation on the regional risk values of all the regions byadopting a linear judgment manner, to obtain the collision risk value.

A third aspect of the present application provides a control device,including: a transmitter, a receiver, a memory, and a processor;

where the memory is configured to store computer instructions; theprocessor is configured to execute the computer instructions stored inthe memory to implement the collision detection method based on anautonomous vehicle provided by any of the implementations of the firstaspect.

A fourth aspect of the present application provides a storage medium,including: a readable storage medium and computer instructions, wherethe computer instructions are stored in the readable storage medium; thecomputer instructions are configured to implement the collisiondetection method based on an autonomous vehicle provided by any of theimplementations of the first aspect.

The collision detection method and apparatus based on an autonomousvehicle, the device and the storage medium provided by the embodiment ofthe present application acquire first point cloud data of each obstaclein each region around the autonomous vehicle, where the first pointcloud data represents coordinate information of the obstacle and thefirst point cloud data is based on a world coordinate system; convertthe first point cloud data of each obstacle into second point cloud databased on a relative coordinate system, where an origin of the relativecoordinate system is a point on the autonomous vehicle; determine,according to the second point cloud data of each obstacle in allregions, a collision risk value, where the collision risk valuerepresents a possibility of collision of the autonomous vehicle.Therefore, the possibility of collision of the autonomous vehicle isjudged in real time and accurately during the operation of theautonomous vehicle. In addition, the solution provides a de-positioningmanner for collision detection without depending on the world coordinatesystem, and without depending on more modules or on subsystems based onparameters of the world coordinate system, thereby improving thereliability and stability of collision detection. Moreover, when thepositioning system of the autonomous vehicle fails, the collisiondetection can be completed by adopting this solution.

BRIEF DESCRIPTION OF THE DRAWINGS

In order to illustrate the technical solutions in the embodiments of thepresent application or the prior art more clearly, a brief descriptionof the drawings required in description of the embodiment or the priorart will be given below. Obviously, the drawings in the followingdescription are some embodiments of the present application, and otherdrawings can be obtained according to these drawings without anycreative efforts for those skilled in the art.

FIG. 1 is a flowchart of a collision detection method based on anautonomous vehicle according to an embodiment of the presentapplication;

FIG. 2 is a Region Division Diagram I in a collision detection methodbased on an autonomous vehicle according to an embodiment of the presentapplication;

FIG. 3 is a Region Division Diagram II in a collision detection methodbased on an autonomous vehicle according to an embodiment of the presentapplication;

FIG. 4 is a flowchart of another collision detection method based on anautonomous vehicle according to an embodiment of the presentapplication;

FIG. 5 is a schematic structural diagram of a collision detectionapparatus based on an autonomous vehicle according to an embodiment ofthe present application;

FIG. 6 is a schematic structural diagram of another collision detectionapparatus based on an autonomous vehicle according to an embodiment ofthe present application;

FIG. 7 is a schematic structural diagram of a control device accordingto an embodiment of the present application.

DESCRIPTION OF EMBODIMENTS

In order to make the purpose, technical solution and advantages of theembodiment of the present application clearer, a clear and completedescription of the technical solution in the embodiment of the presentapplication will be given below with reference to the attached drawingsin the embodiment of the present application. Obviously, the describeddrawings are some of the embodiments of the present application ratherthan all of them. All other drawings obtained by those skilled based onthe embodiment of the present application without any creative effortfall within the scope of protection of the present application.

In the prior art, laser radar can be adopted to collect positioninformation of obstacles of an autonomous vehicle, the positioninformation is a kind of point cloud data that is based on ahigh-precision world coordinate system, and the possibility of collisionof the autonomous vehicle can then be calculated according to the pointcloud data.

However, in the prior art, when determining the possibility of collisionof an autonomous vehicle, the world coordinate system needs to be usedto determine the possibility of collision of the autonomous vehicle, butwhen using the world coordinate system, it is necessary to depend on apositioning module, and further depend on more modules indirectly; andthe subsystem which is used to calculate the parameters based on theworld coordinate system will bring some unreliability, thereby resultingin unreliable collision detection.

For the above problem, the present application provides a methodcollision detection method and apparatus based on an autonomous vehicle,a device and a storage medium that provide a de-positioning manner forcollision detection without depending on the world coordinate system,and without depending on more modules or on subsystems based onparameters of the world coordinate system, thereby improving thereliability and stability of collision detection. Moreover, when thepositioning system of the autonomous vehicle fails, the collisiondetection can be completed by adopting this solution. The solution willbe described in detail below through several specific embodiments.

FIG. 1 is a flowchart of a collision detection method based on anautonomous vehicle according to an embodiment of the presentapplication. As shown in FIG. 1 , the execution body of the solution isa controller of the autonomous vehicle, a control device of an automaticdriving system of the autonomous vehicle, and the like. The collisiondetection method based on an autonomous vehicle includes:

Step 101, acquiring first point cloud data of each obstacle in eachregion around the autonomous vehicle, where the first point cloud datarepresents coordinate information of the obstacle, and the first pointcloud data is based on a world coordinate system.

In this step, specifically, the present embodiment is described bytaking a controller of the autonomous vehicle as the execution body.

A detection apparatus is set on the autonomous vehicle, and thedetection apparatus may be any one of the following: a radar sensor of amain automatic driving system, an independent radar sensor, ultrasonicradar, and millimeter wave radar. The detection apparatus can detect thesurrounding environment of the autonomous vehicle and acquire pointcloud data of obstacles surrounding the autonomous vehicle.

Since there is more than one obstacle around the autonomous vehicle, thesurrounding region of the autonomous vehicle can be divided into aplurality of regions. For example, the front side, the rear side, theleft side and the right side of the autonomous vehicle are divided to beone region, respectively, to obtain four regions; alternatively, arectangular coordinate system is established with the center point ofthe autonomous vehicle as the origin, a straight line along thefront-rear direction of the autonomous vehicle as the X-axis and astraight line passing through the center point and being perpendicularto the X-axis as the Y-axis, and the region in the rectangularcoordinate system is divided into a plurality of blocks to obtain aplurality of regions. The present application is not limited to theregion division manner for the surrounding region of the autonomousvehicle.

For example, FIG. 2 is a Region Division Diagram I in a collisiondetection method based on an autonomous vehicle according to anembodiment of the present application. As shown in FIG. 2 , thesurrounding region of the autonomous vehicle is divided to obtain region1, region 2, region 3, and region 4.

For another example, FIG. 3 is a Region Division Diagram II of acollision detection method based on an autonomous vehicle according toan embodiment of the present application. As shown in FIG. 3 , thesurrounding region of the autonomous vehicle is divided to obtain region1, region 2, region 3, region 4, region 5, region 6, region 7, andregion 8.

During the driving process of the autonomous vehicle, for example, thedetection apparatus detects first point cloud data of each obstacle whenthe autonomous vehicle is going straight or turning and an obstacleappears near the autonomous vehicle, or when the positioning system ofthe autonomous vehicle fails. Since the region surrounding theautonomous vehicle is divided, the controller of the autonomous vehiclecan map each obstacle to a region after the controller of the autonomousvehicle acquires the first point cloud data of each obstacle detected bythe detection apparatus, and can thus obtain the first point cloud dataof each obstacle in each region.

First point cloud data represents coordinate information of an obstaclecorresponding to the first point cloud data, and the first point clouddata is based on a world coordinate system.

For example, based on the region division diagram of FIG. 2 , thecontroller of the autonomous vehicle acquires the first point cloud dataof an obstacle a, the first point cloud data of an obstacle b and thefirst point cloud data of an obstacle c in the region 1, acquires thefirst point cloud data of an obstacle d and the first point cloud dataof an obstacle e in the region 2, acquires the first point cloud data ofan obstacle f and the first point cloud data of an obstacle g in theregion 3, and acquires the first point cloud data of an obstacle h andthe first point cloud data of an obstacle i in the region 4.

Step 102, converting the first point cloud data of each obstacle intosecond point cloud data based on a relative coordinate system, where anorigin of the relative coordinate system is a point on the autonomousvehicle.

Optionally, the origin of the relative coordinate system is a centerpoint of the autonomous vehicle, an X-axis of the relative coordinatesystem is a central axis of the autonomous vehicle, a Y-axis of therelative coordinate system passes through the origin and isperpendicular to the X-axis, the Z-axis of the relative coordinatesystem passes through the origin, and the Z-axis of the relativecoordinate system is perpendicular to both the X-axis and the Y-axis.

In this step, specifically, the controller of the autonomous vehicleconverts the acquired first point cloud data of each obstacle into thesecond point cloud data based on the relative coordinate system, wherethe origin of the relative coordinate system is a point on theautonomous vehicle.

Preferably, the origin of the relative coordinate system is the centerpoint of the autonomous vehicle, the center axis of the autonomousvehicle is taken as the X-axis of the relative coordinate system, astraight line passing through the origin and being perpendicular to theX-axis is taken as the Y-axis of the relative coordinate system, and thestraight line passing through the origin and being perpendicular to boththe X-axis and the Y-axis is taken as the Z-axis of the relativecoordinate system, thus a relative coordinate system is established.

Step 103, determining a collision risk value according to the secondpoint cloud data of each obstacle in all regions, where the collisionrisk value represents a possibility of collision of the autonomousvehicle.

In this step, specifically, since the second point cloud data of eachobstacle is based on the above relative coordinate system, the secondpoint cloud data represents the position information between theobstacle and the autonomous vehicle, thus the controller of theautonomous vehicle can judge the possibility of collision of theautonomous vehicle according to the second point cloud data of eachobstacle in all regions, thereby determining the collision risk value.

The present embodiment acquires first point cloud data of each obstaclein each region around the autonomous vehicle, where the first pointcloud data represents coordinate information of the obstacle and thefirst point cloud data is based on a world coordinate system; convertsthe first point cloud data of each obstacle into second point cloud databased on a relative coordinate system, where an origin of the relativecoordinate system is a point on the autonomous vehicle; determines,according to the second point cloud data of each obstacle in allregions, a collision risk value, where the collision risk valuerepresents a possibility of collision of the autonomous vehicle.Therefore, the possibility of collision of the autonomous vehicle isjudged in real time and accurately during the operation of theautonomous vehicle. In addition, the solution provides a de-positioningmanner for collision detection without depending on the world coordinatesystem and without depending on more modules or on subsystems based onparameters of the world coordinate system, thereby improving thereliability and stability of collision detection. Moreover, when thepositioning system of the autonomous vehicle fails, the collisiondetection can be completed by adopting this solution.

FIG. 4 is a flowchart of another collision detection method based on anautonomous vehicle according to an embodiment of the presentapplication. As shown in FIG. 4 , the execution body of the solution isa controller of the autonomous vehicle, a control device of an automaticdriving system of the autonomous vehicle, or the like. The collisiondetection method based on an autonomous vehicle includes:

Step 201, acquiring first point cloud data of each obstacle in eachregion around the autonomous vehicle, where the first point cloud datarepresents coordinate information of the obstacle, and the first pointcloud data is based on a world coordinate system.

In this step, specifically, this step may refer to step 101 shown inFIG. 1 , which will not be repeated herein.

Step 202, converting the first point cloud data of each obstacle intosecond point cloud data based on a relative coordinate system, where theorigin of the relative coordinate system is a point on the autonomousvehicle.

Optionally, the origin of the relative coordinate system is the centerpoint of the autonomous vehicle, an X-axis of the relative coordinatesystem is a central axis of the autonomous vehicle, a Y-axis of therelative coordinate system passes through the origin and isperpendicular to the X-axis, a Z-axis of the relative coordinate systempasses through the origin, and the Z-axis of the relative coordinatesystem is perpendicular to both the X-axis and the Y-axis.

In this step, specifically, refer to step 102 shown in FIG. 1 for thisstep, which will not be repeated herein.

Step 203, determining, according to the second point cloud data of eachobstacle, an obstacle speed of each obstacle.

The step 203 specifically includes:

Step 2031, determining, according to the second point cloud data of eachobstacle on at least two frames, a displacement value of each obstacle.

Step 2032, determining, according to both the displacement value of eachobstacle and times corresponding to the at least two frames, theobstacle speed of each obstacle.

In this step, specifically, the controller of the autonomous vehicle canacquire the second point cloud data of each obstacle on multiple frames.For each obstacle in each region, the controller of the autonomousvehicle determines the displacement value of the obstacle according tothe second point cloud data of the obstacle within different frames, andthe displacement value represents the displacement of the obstacle in acertain time. Since the frame corresponds to the time, the controller ofthe autonomous vehicle can determine the time corresponding to thedisplacement value. The controller of the autonomous vehicle determinesthe obstacle speed of the obstacle according to both the displacementvalue and time of the obstacle.

For example, based on the region division diagram of FIG. 2 , thecontroller of the autonomous vehicle acquires the first point cloud data1 of the obstacle a in the region 1 on the first frame, and can acquirethe first point cloud data 2 of the obstacle a in the region 1 on thesecond frame over time. The controller of the autonomous vehicleconverts the first point cloud data 1 into the second point cloud data1, and converts the first point cloud data 2 into the second point clouddata 2. Since each second point cloud data represents the distanceinformation between the obstacle and the autonomous vehicle, and thesecond point cloud data is based on the above described relativecoordinate system, the controller of the autonomous vehicle candetermine the displacement information of the obstacle a according tothe second point cloud data 1 and the second point cloud data 2, thatis, can determine the displacement value; the time difference betweenthe first frame and the second frame is taken as the time; thecontroller of the autonomous vehicle divides the displacement value bytime to obtain the obstacle speed of the obstacle a.

Step 204, acquiring the obstacle speed of each obstacle on previous Nframes, where N is a positive integer great than or equal to 1.

In this step, specifically, the obstacle speed of each obstacle can becorrected. After calculating the obstacle speed of each obstacle on eachframe in the above manner, the obstacle speed of each obstacle on theprevious N frames can be obtained.

Step 205, correcting, according to the obstacle speed of each obstacleon the previous N frames, the obstacle speed of each obstacle to obtaina corrected obstacle speed of each obstacle.

In this step, specifically, after the controller of the autonomousvehicle calculates the obstacle speed of each obstacle on the currentframe, the controller of the autonomous vehicle judges whether theobstacle speed of each obstacle on the current frame has changedabruptly according to the obstacle speed of each obstacle on theprevious N frames; if it is determined that the obstacle speed of eachobstacle on the current frame is too large or too small, it can bedetermined that the calculation of the obstacle speed of each obstacleon the current frame is incorrect. Then, the controller of theautonomous vehicle can adopt an average value or a weighted averagevalue of the obstacle speed of each obstacle on the previous N frames asthe obstacle speed of each obstacle on the current frame, therebycorrecting the obstacle speed of each obstacle on the current frame.

For example, based on the region division diagram of FIG. 2 , thecontroller of the autonomous vehicle acquires that the obstacle speed ofthe obstacle a in the region 1 on the first frame is 2 m/s, the obstaclespeed of the obstacle a in the region 1 on the second frame is 2.1 m/s,and the obstacle speed of the obstacle a in the region 1 on the thirdframe is 1.9 m/s; the controller of the autonomous vehicle acquires thatthe obstacle speed of the obstacle a in the region 1 on the fourth frameis 9 m/s, then the controller of the autonomous vehicle determines thatthe speed of the obstacle a on the fourth frame is inaccurate; thecontroller of the autonomous vehicle can calculate the average value ofthe obstacle speed of the obstacle a on the first three frames, andobtain the corrected obstacle speed of the obstacle a in the region 1 onthe fourth frame of 2 m/s.

Step 206, determining a regional risk value of each region according tothe obstacle speeds of all obstacles in each region.

The step 206 includes following implementations:

A first implementation of step 206 includes step 2061 a:

Step 2061 a, performing a weighted calculation on the obstacle speeds ofall obstacles in each region to obtain the regional risk value of eachregion.

A second implementation of step 206 includes steps 2061 b to 2063 b:

Step 2061 b, determining, according to the obstacle speeds of allobstacles in each region, a test obstacle in each region.

Step 2062 b, acquiring an actual distance and a safety distance of thetest obstacle in each region, where the actual distance represents theactual distance between the test obstacle and the autonomous vehicle,and the safety distance represents the safety distance between the testobstacle and the autonomous vehicle.

The step 2062 b specifically includes:

determining, according to the second point cloud data of the testobstacle in each region, the actual distance of the test obstacle ineach region; acquiring an autonomous vehicle acceleration and anautonomous vehicle speed of the autonomous vehicle, and acquiring anobstacle acceleration of the test obstacle in each region; determining,according to the obstacle acceleration of the test obstacle in eachregion, the obstacle speed of the test obstacle in each region, and theautonomous vehicle acceleration and the autonomous vehicle speed, thesafety distance of the test obstacle in each region.

Step 2063 b, determining a difference between the actual distance andthe safety distance of the test obstacle in each region as the regionalrisk value of each region.

In this step, specifically, for each region, the controller of theautonomous vehicle determines the regional risk value of the regionaccording to the obstacle speeds of the obstacles in the region.

The first implementation of this step is that, for each region, thecontroller of the autonomous vehicle performs a weighted calculation onthe obstacle speeds of all obstacles in the region to obtain theregional risk value of the region.

For example, based on the region division diagram of FIG. 2 , thecontroller of the autonomous vehicle acquires the second point clouddata of the obstacle a, the second point cloud data of the obstacle band the second point cloud data of the obstacle c in the region 1; thecontroller of the autonomous vehicle calculates the obstacle speed a ofthe obstacle a, the obstacle speed b of the obstacle b and the obstaclespeed c of the obstacle c in the region 1; then, the controller of theautonomous vehicle performs a weighted calculation on the obstacle speeda, the obstacle speed b and the obstacle speed c to obtain the regionalrisk value of the region 1.

The second implementation of this step is that, for each region, thecontroller of the autonomous vehicle determines, according to theobstacle speeds of all obstacles in the region on the current frame, theobstacle with the highest obstacle speed as the test obstacle in theregion; alternatively, the controller of the autonomous vehicleperforms, according to the obstacle speed of each obstacle in the regionon the current frame and the second point cloud data of each obstacle inthe region on the current frame, a weighted calculation on the obstaclespeed and the second point cloud data of each obstacle in the region toobtain a parameter and then determine a maximum parameter, and thecontroller of the autonomous vehicle takes the obstacle corresponding tothe maximum parameter as the test obstacle in the region. Alternatively,for each region, the controller of the autonomous vehicle determines,according to the second point cloud data of each obstacle in the region,the second point cloud data closest to the autonomous vehicle in theregion, and determines the obstacle corresponding to the second pointcloud data closest to the autonomous vehicle as the test obstacle in theregion.

Then, for each region, the controller of the autonomous vehicle candirectly determine, according to the second point cloud data of the testobstacle, the actual distance between the autonomous vehicle and thetest obstacle in the region. Moreover, for each region, the controllerof the autonomous vehicle calculates, according to the acceleration ofthe autonomous vehicle, the speed of the autonomous vehicle, theacceleration of the test obstacle and the speed of the test obstacle,the safety distance between the autonomous vehicle and the test obstaclein the region by an existing physical displacement calculation manner.

Then, for each region, the controller of the autonomous vehiclecalculates, according to the actual distance between the autonomousvehicle and the test obstacle in the region and the safety distancebetween the autonomous vehicle and the test obstacle in the region, thedifference between the actual distance of the test obstacle and thesafety distance of the test obstacle, and takes the difference as theregional risk value of the region, where the larger the differencebetween the actual distance and the safety distance, the smaller theregional risk value of the region.

For example, based on the region division diagram of FIG. 2 , thecontroller of the autonomous vehicle acquires the second point clouddata 1 of the obstacle a, the second point cloud data 2 of the obstacleb and the second point cloud data 3 of the obstacle c in the region 1;the controller of the autonomous vehicle calculates the obstacle speed aof the obstacle a, the obstacle speed b of the obstacle b and theobstacle speed c of the obstacle c in the region 1; the controller ofthe autonomous vehicle performs a weighted calculation on the secondpoint cloud data 1 and the obstacle speed a of the obstacle a to obtaina parameter 1; the controller of the autonomous vehicle performs aweighted calculation on the second point cloud data 2 and the obstaclespeed b of the obstacle b to obtain a parameter 2; the controller of theautonomous vehicle performs a weighted calculation on the second pointcloud data 3 and the obstacle speed c of the obstacle c to obtain aparameter 3; when the controller of the autonomous vehicle determinesthat the parameter 2 is the largest, it determines that the obstacle bis the test obstacle of the region 1. The controller of the autonomousvehicle can calculate the actual distance and the safety distance of thetest obstacle in the region 1; the controller of the autonomous vehiclecalculates the difference between the actual distance and the safetydistance, and takes the absolute value of the difference as the regionalrisk value of the region 1, or directly takes the difference as theregional risk value of the region 1.

Step 207, determining, according to the regional risk values of allregions, a collision risk value, where the collision risk valuerepresents a possibility of collision of the autonomous vehicle.

The step 207 includes following implementations:

A first implementation of step 207: performing, according to a presetcollision risk weight corresponding to each region one to one, aweighted calculation on the regional risk values of all regions toobtain the collision risk value.

A second implementation of step 207: calculating the regional riskvalues of all regions by adopting a linear judgment manner to obtain thecollision risk value.

In this step, specifically, the controller of the autonomous vehicledetermines, according to the regional risk values of all regions, thecollision risk value, where the collision risk value represents thepossibility of collision of the autonomous vehicle.

The first implementation of this step is that, the controller of theautonomous vehicle divides the surroundings of the autonomous vehicleinto a plurality of regions, and each region corresponds to a collisionrisk weight. The collision risk weight is a preset value, that is, anempirical value. The controller of the autonomous vehicle performs,according to the regional risk value of each region and the collisionrisk weight of each region, a weighted calculation on the regional riskvalues of all regions, and takes the obtained data value as thecollision risk value.

For example, based on the region division diagram of FIG. 2 , thecontroller of the autonomous vehicle can calculate the regional riskvalue 1 of the region 1, the regional risk value 2 of the region 2, theregional risk value 3 of the region 3 and the regional risk value 4 ofthe region 4 by adopting the above calculation manner. A collision riskweight 1 is set for the region 1, a collision risk weight 2 is set forthe region 2, a collision risk weight 3 is set for the region 3, and acollision risk weight 4 is set for the region 4. The controller of theautonomous vehicle adopts the formula of (regional risk value1×collision risk weight 1+regional risk value 2×collision risk weight2+regional risk value 3×collision risk weight 3+regional risk value4×collision risk weight 4)/4 for calculation and obtains a data valuethat is the collision risk value of the autonomous vehicle.

The second implementation of this step is that, the controller of theautonomous vehicle adopts a linear judgment manner to calculate thecollision risk value according to the regional risk values of allregions. The controller of the autonomous vehicle can adopt a linearjudgment manner to calculate the collision risk value according to theregional risk value of each region and the collision risk weight of eachregion.

For example, upon the region division diagram of FIG. 2 , the controllerof the autonomous vehicle can calculate the regional risk value 1 of theregion 1, the regional risk value 2 of the region 2, the regional riskvalue 3 of the region 3 and the regional risk value 4 of the region 4 byadopting the above calculation manner. The collision risk weight 1 isset for the region 1, the collision risk weight 2 is set for the region2, the collision risk weight 3 is set for the region 3, and thecollision risk weight 4 is set for the region 4. The controller of theautonomous vehicle calculates that (regional risk value 1×collision riskweight 1+regional risk value 3×collision risk weight 3) equals to datavalue 1, and (regional risk value 2×collision risk weight 2+regionalrisk value 3×collision risk weight 3) equals to data value 2. If thecontroller of the autonomous vehicle determines that the data value 1 isgreater than the data value 2, it determines that the collision riskvalue is a; if the controller of the autonomous vehicle determines thatthe data value 1 is less than or equal to the data value 2, itdetermines that the collision risk value is b.

The present embodiment detects the first point cloud data of eachobstacle, and the first point cloud data represents coordinateinformation of obstacles surrounding the autonomous vehicle, where thecoordinate information of the obstacles is based on a world coordinatesystem; the controller converts the first point cloud data into thesecond point cloud data based on a relative coordinate system; thecontroller finally determines, according to the second point cloud dataof each obstacle in each region, the collision risk value, where thecollision risk value represents the possibility of collision of theautonomous vehicle. Therefore, thereby the possibility of collision ofthe autonomous vehicle is judged in real time and accurately during theoperation of the autonomous vehicle. In addition, the solution providesa de-positioning manner for collision detection without depending on theworld coordinate system and without depending on more modules or onsubsystems based on parameters of the world coordinate system, therebyimproving the reliability and stability of collision detection.Moreover, when the positioning system of the autonomous vehicle fails,the collision detection can be completed by adopting this solution.

FIG. 5 is a schematic structural diagram of a collision detectionapparatus based on an autonomous vehicle according to an embodiment ofthe present application. As shown in FIG. 5 , the collision detectionapparatus based on an autonomous vehicle provided by the presentembodiment includes:

an acquisition unit 51, configured to acquire first point cloud data ofeach obstacle in each region around the autonomous vehicle, where thefirst point cloud data represents coordinate information of theobstacle, and the first point cloud data is based on a world coordinatesystem;

a conversion unit 52, configured to convert the first point cloud dataof the each obstacle into second point cloud data based on a relativecoordinate system, where an origin of the relative coordinate system isa point on the autonomous vehicle;

a determination unit 53, configured to determine, according to thesecond point cloud data of the each obstacle in all regions, a collisionrisk value, where the collision risk value represents the possibility ofcollision of the autonomous vehicle.

The collision detection apparatus based on an autonomous vehicleprovided by the present embodiment is the same as the technical solutionin the collision detection method based on an autonomous vehicleprovided by any one of the previous embodiments, and the implementationprinciple thereof is similar, which will not be repeated herein.

The present embodiment acquires first point cloud data of each obstaclein each region around the autonomous vehicle, where the first pointcloud data represents coordinate information of the obstacle and thefirst point cloud data is based on a world coordinate system; convertsthe first point cloud data of the each obstacle into second point clouddata based on a relative coordinate system, where an origin of therelative coordinate system is a point on the autonomous vehicle;determines, according to the second point cloud data of the eachobstacle in all regions, a collision risk value, where the collisionrisk value represents the possibility of collision of the autonomousvehicle. Therefore, the possibility of collision of the autonomousvehicle is judged in real time and accurately during the operation ofthe autonomous vehicle. In addition, the solution provides ade-positioning manner for collision detection without depending on theworld coordinate system and without depending on more modules or onsubsystems based on parameters of the world coordinate system, therebyimproving the reliability and stability of collision detection.Moreover, when the positioning system of the autonomous vehicle fails,the collision detection can be completed by adopting this solution.

FIG. 6 is a schematic structural diagram of another collision detectionapparatus based on an autonomous vehicle according to an embodiment ofthe present application. On the basis of the embodiment shown in FIG. 5, as shown in FIG. 6 , for the collision detection apparatus based on anautonomous vehicle provided by the present embodiment, the origin of therelative coordinate system is the center point of the autonomousvehicle, the X-axis of the relative coordinate system is the centralaxis of the autonomous vehicle, the Y-axis of the relative coordinatesystem passes through the origin and is perpendicular to the X-axis, theZ-axis of the relative coordinate system passes through the origin, andthe Z-axis of the relative coordinate system is perpendicular to boththe X-axis and the Y-axis.

The determination unit 53 includes:

a first determination module 531, configured to determine, according tothe second point cloud data of the each obstacle, the obstacle speed ofthe each obstacle;

a second determination module 532, configured to determine, according tothe obstacle speeds of all obstacles in the each region, the regionalrisk value of the each region;

a third determination module 533, configured to determine, according tothe regional risk values of all regions, the collision risk value.

The first determination module 531 includes:

a first determination sub-module 5311, configured to determine,according to the second point cloud data of the each obstacle on atleast two frames, a displacement value of the each obstacle;

a second determination sub-module 5312, configured to determine,according to both the displacement value of the each obstacle and thetime corresponding to the at least two frames, the obstacle speed of theeach obstacle.

The determination unit 53 further includes:

an acquisition module 534, configured to acquire the obstacle speed ofthe each obstacle on previous N frames after the first determinationmodule 531 determines the obstacle speed of the each obstacle accordingto the second point cloud data of the each obstacle, where N is apositive integer great than or equal to 1;

a correction module 535, configured to correct, according to theobstacle speed of the each obstacle on the previous N frames, theobstacle speed of the each obstacle, to obtain the corrected obstaclespeed of the each obstacle.

The second determination module 532 includes a calculation sub-module5321, configured to perform a weighted calculation on the obstaclespeeds of all obstacles in each region to obtain the regional risk valueof each region.

Or, the second determination module 532 includes:

a third determination sub-module 5322, configured to determine,according to the obstacle speeds of all obstacles in the each region, atest obstacle in the each region;

an acquisition sub-module 5323, configured to acquire an actual distanceand a safety distance of the test obstacle in each region, where theactual distance represents the actual distance between the test obstacleand the autonomous vehicle, and the safety distance represents thesafety distance between the test obstacle and the autonomous vehicle;

a confirmation sub-module 5324, configured to determine a differencebetween the actual distance and the safety distance of the test obstaclein each region as the regional risk value of each region.

The acquisition sub-module 5323 is specifically configured to determine,according to the second point cloud data of the test obstacle in theeach region, the actual distance of the test obstacle in the eachregion.

Or, the acquisition sub-module 5323 is specifically configured toacquire an autonomous vehicle acceleration and an autonomous vehiclespeed of the autonomous vehicle and acquire an obstacle acceleration ofthe test obstacle in each region; determine, according to the obstacleacceleration of the test obstacle in the each region, the obstacle speedof the test obstacle in the each region, and the autonomous vehicleacceleration and the autonomous vehicle speed, the safety distance ofthe test obstacle in the each region.

The third determination module 533 is specifically configured toperform, according to a preset collision risk weight corresponding tothe each region in a one-to-one relationship, a weighted calculation onthe regional risk values of all regions to obtain the collision riskvalue.

Or, the third determination module 533 is specifically configured toperform a calculation on the regional risk values of all regions byadopting a linear judgment manner, to obtain the collision risk value.

The collision detection apparatus based on an autonomous vehicleprovided by the present embodiment is the same as the technical solutionin the collision detection method based on an autonomous vehicleprovided by any one of the previous embodiments, and the implementationprinciple thereof is similar, which will not be repeated herein.

The present embodiment detects the first point cloud data of eachobstacle, and the first point cloud data represents coordinateinformation of obstacles surrounding the autonomous vehicle, where thecoordinate information of the obstacles is based on a world coordinatesystem; the controller converts the first point cloud data into thesecond point cloud data based on a relative coordinate system; thecontroller finally determines, according to the second point cloud dataof each obstacle in each region, the collision risk value, where thecollision risk value represents the possibility of collision of theautonomous vehicle. Therefore, the possibility of collision of theautonomous vehicle is judged in real time and accurately during theoperation of the autonomous vehicle. In addition, the solution providesa de-positioning manner for collision detection without depending on theworld coordinate system and without depending on more modules or onsubsystems based on parameters of the world coordinate system, therebyimproving the reliability and stability of collision detection.Moreover, when the positioning system of the autonomous vehicle fails,the collision detection can be completed by adopting this solution.

FIG. 7 is a schematic structural diagram of a control device provided byan embodiment of the present application. As shown in FIG. 7 , thecontrol device includes a transmitter 71, a receiver 72, a memory 73 anda processor 74.

The memory 73 is configured to store computer instructions; theprocessor 74 is configured to execute the computer instructions storedin the memory 73 to implement the technical solution of the collisiondetection method based on an autonomous vehicle of any implementationprovided by the previous embodiment.

The present application also provides a storage medium, comprising: areadable storage medium and computer instructions stored in a readablestorage medium; the computer instructions are used to implement thetechnical solution of the collision detection method based on anautonomous vehicle of any implementation provided by the previousembodiment.

In the specific implementation of the control device described above, itshould be understood that the processor 74 may be a Central ProcessingUnit (CPU), or may be other general processors, a Digital SignalProcessor (DSP), an Application Specific Integrated Circuit (ASIC), etc.The general processor may be a microprocessor, or the processor may beany conventional processor or the like. Steps of the method disclosed inthe embodiment of the present application may be directly implemented asa hardware processor, or may be performed by a combination of hardwareand software modules in the processor.

One of ordinary skill in the art will appreciate that all or part of thesteps to implement each method embodiment described above may beaccomplished by hardware associated with the program instructions. Theabove described program can be stored in a computer readable storagemedium. When the program is executed, the steps including the abovedescribed method embodiment are performed; and the described storagemedium includes: a read-only memory (ROM), a RAM, a flash memory, a harddisk, a solid state hard disk, a magnetic tape, a floppy disk, anoptical disc, and any combination thereof.

Finally, it should be noted that the above embodiments are merelyillustrative of the technical solutions of the present application, andare not to be taken in a limiting sense; although the presentapplication has been described in detail with reference to the aboveembodiments, those skilled in the art shall understand that they maystill modify the technical solutions described in the above embodiments,or equivalently substitute some or all of the technical features; andthe modifications or substitutions do not deviate the nature of thecorresponding technical solutions from the range of the technicalsolutions of each embodiment of the present application.

What is claimed is:
 1. A collision detection method, implemented by acontroller of an autonomous vehicle, comprising: acquiring first pointcloud data of each obstacle in each region around the autonomousvehicle, wherein the first point cloud data represents coordinateinformation of the each obstacle, and the first point cloud data isbased on a world coordinate system; converting the first point clouddata of the each obstacle into second point cloud data based on arelative coordinate system, wherein an origin of the relative coordinatesystem is a point on the autonomous vehicle; determining, according tothe second point cloud data of the each obstacle, an obstacle speed ofthe each obstacle; determining, according to obstacle speeds of allobstacles within the each region, a test obstacle in the each region;acquiring an actual distance and a safety distance of the test obstaclein the each region, wherein the actual distance represents the actualdistance between the test obstacle and the autonomous vehicle, and thesafety distance represents the safety distance between the test obstacleand the autonomous vehicle; determining a difference between the actualdistance and the safety distance of the test obstacle in the each regionas a regional risk value of the each region; determining, according tothe regional risk value of the each region, a collision risk value,wherein the collision risk value represents a possibility of collisionof the autonomous vehicle; and controlling driving of the autonomousvehicle, based on the determined collision risk value of the automaticvehicle, wherein the determining, according to obstacle speeds of allobstacles within the each region, a test obstacle in the each region,comprises: performing a weighted calculation on the obstacle speed ofeach obstacle within the each region on a current frame andcorresponding second point cloud data of the each obstacle on thecurrent frame, to obtain parameters respectively corresponding to theall obstacles within the each region; determining a maximum parameterfrom the obtained parameters; and taking an obstacle within the eachregion that corresponds to the maximum parameter, as the test obstaclein the each region.
 2. The method of claim 1, wherein the origin of therelative coordinate system is a center point of the autonomous vehicle,an X-axis of the relative coordinate system is a central axis of theautonomous vehicle, a Y-axis of the relative coordinate system passesthrough the origin and is perpendicular to the X-axis, a Z-axis of therelative coordinate system passes through the origin, and the Z-axis ofthe relative coordinate system is perpendicular to both the X-axis andthe Y-axis.
 3. The method of claim 1, wherein after the determining,according to the second point cloud data of the each obstacle, theobstacle speed of the each obstacle on the current frame, the methodfurther comprises: acquiring obstacle speeds of the each obstacle onprevious N frames, wherein N is a positive integer great than or equalto 1; and correcting, according to the obstacle speeds of the eachobstacle on the previous N frames, the obstacle speed of the eachobstacle on the current frame, to obtain a corrected obstacle speed ofthe each obstacle on the current frame.
 4. The method of claim 3,wherein the correcting, according to the obstacle speeds of the eachobstacle on the previous N frames, the obstacle speed of the eachobstacle on the current frame to obtain a corrected obstacle speed ofthe each obstacle on the current frame, comprises: calculating aweighted average value of the obstacle speeds of the each obstacle onthe previous N frames, as the corrected obstacle speed of the eachobstacle on the current frame.
 5. The method of claim 1, wherein theacquiring a safety distance of the test obstacle in the each regioncomprises: acquiring an autonomous vehicle acceleration and anautonomous vehicle speed of the autonomous vehicle and acquiring anobstacle acceleration of the test obstacle in the each region; anddetermining, according to the obstacle acceleration of the test obstaclein the each region, the obstacle speed of the test obstacle in the eachregion, and the autonomous vehicle acceleration and the autonomousvehicle speed, the safety distance of the test obstacle in the eachregion.
 6. The method of claim 1, wherein the determining, according tothe regional risk value of the each region, the collision risk valuecomprises: performing a calculation on the regional risk values of allregions by adopting a linear judgment manner to obtain the collisionrisk value.
 7. A non-transitory storage medium, comprising: anon-transitory readable storage medium and computer instructions,wherein the computer instructions are stored in the readable storagemedium; and the computer instructions are configured to cause aprocessor to implement the collision detection method according toclaim
 1. 8. The method of claim 1, wherein the determining, according toa regional risk value of the each region, the collision risk valuecomprises: performing a weighted calculation on the regional risk valuesof all regions, wherein the each region is multiplied by a correspondingpreset collision risk weight in the weighted calculation; and taking adata value obtained from the weighted calculation, as the collision riskvalue.
 9. A collision detection apparatus, implemented in an autonomousvehicle, comprising: a memory and a processor; wherein the memory isconfigured to store executable instructions of the processor; and theprocessor, when executing the executable instructions, is configured to:acquire first point cloud data of each obstacle in each region aroundthe autonomous vehicle, wherein the first point cloud data representscoordinate information of the each obstacle, and the first point clouddata is based on a world coordinate system; convert the first pointcloud data of the each obstacle into second point cloud data based on arelative coordinate system, wherein an origin of the relative coordinatesystem is a point on the autonomous vehicle; determine, according to thesecond point cloud data of the each obstacle, an obstacle speed of theeach obstacle; determine, according to the obstacle speeds of allobstacles in the each region, a test obstacle in the each region;acquire an actual distance and a safety distance of the test obstacle inthe each region, wherein the actual distance represents the actualdistance between the test obstacle and the autonomous vehicle, and thesafety distance represents the safety distance between the test obstacleand the autonomous vehicle; determine a difference between the actualdistance and the safety distance of the test obstacle in the each regionas a regional risk value of the each region; determine, according to theregional risk values of the each region, a collision risk value, whereinthe collision risk value represents the possibility of collision of theautonomous vehicle; and control driving of the autonomous vehicle, basedon the determined collision risk value of the automatic vehicle, whereinthe test obstacle in the each region is determined by: performing aweighted calculation on the obstacle speed of each obstacle within theeach region on a current frame and corresponding second point cloud dataof the each obstacle on the current frame, to obtain parametersrespectively corresponding to the all obstacles within the each region;determining a maximum parameter from the obtained parameters; and takingan obstacle within the each region that corresponds to the maximumparameter, as the test obstacle in the each region.
 10. The apparatus ofclaim 9, wherein the origin of the relative coordinate system is acenter point of the autonomous vehicle, an X-axis of the relativecoordinate system is a central axis of the autonomous vehicle, a Y-axisof the relative coordinate system passes through the origin and isperpendicular to the X-axis, a Z-axis of the relative coordinate systempasses through the origin, and the Z-axis of the relative coordinatesystem is perpendicular to both the X-axis and the Y-axis.
 11. Theapparatus of claim 9, wherein the processor is further configured to:determine, according to the second point cloud data of the each obstacleon at least two frames, a displacement value of the each obstacle,wherein the displacement value represents displacement of the eachobstacle in a period of time corresponding to the at least two frames;and determine, according to the displacement value of the each obstacleand the period of time corresponding to the at least two frames, theobstacle speed of the each obstacle.
 12. The apparatus of claim 9,wherein the processor is further configured to: acquire obstacle speedsof the each obstacle on previous N frames after the obstacle speed ofthe each obstacle on a current frame is determined according to thesecond point cloud data of the each obstacle, wherein N is a positiveinteger great than or equal to 1; and correct, according to the obstaclespeeds of the each obstacle on the previous N frames, the obstacle speedof the each obstacle on the current frame, to obtain a correctedobstacle speed of the each obstacle on the current frame.
 13. Theapparatus of claim 9, wherein the processor is further configured to:determine, according to the second point cloud data of the test obstaclein the each region, the actual distance of the test obstacle in the eachregion.
 14. The apparatus of claim 9, wherein the processor is furtherconfigured to: acquire an autonomous vehicle acceleration and anautonomous vehicle speed of the autonomous vehicle and acquire anobstacle acceleration of the test obstacle in the each region; anddetermine, according to the obstacle acceleration of the test obstaclein the each region, the obstacle speed of the test obstacle in the eachregion, and the autonomous vehicle acceleration and the autonomousvehicle speed, the safety distance of the test obstacle in the eachregion.
 15. The apparatus of claim 9, wherein the processor is furtherconfigured to: perform, according to a preset collision risk weightcorresponding to the each region in a one-to-one relationship, aweighted calculation on the regional risk values of all the regions, toobtain the collision risk value.
 16. The apparatus of claim 9, whereinthe processor is further configured to: perform a calculation on theregional risk values of all the regions by adopting a linear judgmentmanner to obtain the collision risk value.
 17. The apparatus of claim 9,wherein the processor is further configured to: calculate a weightedaverage value of the obstacle speeds of the each obstacle on theprevious N frames, as the corrected obstacle speed of the each obstacleon the current frame.